Treloar, Neythen J;
Braniff, Nathan;
Ingalls, Brian;
Barnes, Chris P;
(2022)
Deep reinforcement learning for optimal experimental design in biology.
PLoS Computational Biology
, 18
(11)
, Article e1010695. 10.1371/journal.pcbi.1010695.
(In press).
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Abstract
The field of optimal experimental design uses mathematical techniques to determine experiments that are maximally informative from a given experimental setup. Here we apply a technique from artificial intelligence-reinforcement learning-to the optimal experimental design task of maximizing confidence in estimates of model parameter values. We show that a reinforcement learning approach performs favourably in comparison with a one-step ahead optimisation algorithm and a model predictive controller for the inference of bacterial growth parameters in a simulated chemostat. Further, we demonstrate the ability of reinforcement learning to train over a distribution of parameters, indicating that this approach is robust to parametric uncertainty.
Type: | Article |
---|---|
Title: | Deep reinforcement learning for optimal experimental design in biology |
Location: | United States |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1371/journal.pcbi.1010695 |
Publisher version: | https://doi.org/10.1371/journal.pcbi.1010695 |
Language: | English |
Additional information: | Copyright: © 2022 Treloar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Cell and Developmental Biology |
URI: | https://discovery.ucl.ac.uk/id/eprint/10160940 |
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